التفاصيل البيبلوغرافية
| العنوان: |
Phenomapping of patients with heart failure with preserved ejection fraction using machine learning-based unsupervised cluster analysis. |
| المؤلفون: |
Segar, Matthew W., Patel, Kershaw V., Ayers, Colby, Basit, Mujeeb, Tang, W.H. Wilson, Willett, Duwayne, Berry, Jarett, Grodin, Justin L., Pandey, Ambarish |
| المصدر: |
European Journal of Heart Failure; Jan2020, Vol. 22 Issue 1, p148-158, 11p, 3 Charts, 3 Graphs |
| مصطلحات موضوعية: |
HEART failure patients, NATRIURETIC peptides, ALDOSTERONE antagonists, HEART failure, MYOCARDIAL infarction, RESEARCH, RESEARCH methodology, PROGNOSIS, MEDICAL cooperation, EVALUATION research, COMPARATIVE studies, CLUSTER analysis (Statistics), STROKE volume (Cardiac output) |
| مستخلص: |
Aim: To identify distinct phenotypic subgroups in a highly-dimensional, mixed-data cohort of individuals with heart failure (HF) with preserved ejection fraction (HFpEF) using unsupervised clustering analysis.Methods and Results: The study included all Treatment of Preserved Cardiac Function Heart Failure with an Aldosterone Antagonist (TOPCAT) participants from the Americas (n = 1767). In the subset of participants with available echocardiographic data (derivation cohort, n = 654), we characterized three mutually exclusive phenogroups of HFpEF participants using penalized finite mixture model-based clustering analysis on 61 mixed-data phenotypic variables. Phenogroup 1 had higher burden of co-morbidities, natriuretic peptides, and abnormalities in left ventricular structure and function; phenogroup 2 had lower prevalence of cardiovascular and non-cardiac co-morbidities but higher burden of diastolic dysfunction; and phenogroup 3 had lower natriuretic peptide levels, intermediate co-morbidity burden, and the most favourable diastolic function profile. In adjusted Cox models, participants in phenogroup 1 (vs. phenogroup 3) had significantly higher risk for all adverse clinical events including the primary composite endpoint, all-cause mortality, and HF hospitalization. Phenogroup 2 (vs. phenogroup 3) was significantly associated with higher risk of HF hospitalization but a lower risk of atherosclerotic event (myocardial infarction, stroke, or cardiovascular death), and comparable risk of mortality. Similar patterns of association were also observed in the non-echocardiographic TOPCAT cohort (internal validation cohort, n = 1113) and an external cohort of patients with HFpEF [Phosphodiesterase-5 Inhibition to Improve Clinical Status and Exercise Capacity in Heart Failure with Preserved Ejection Fraction (RELAX) trial cohort, n = 198], with the highest risk of adverse outcome noted in phenogroup 1 participants.Conclusions: Machine learning-based cluster analysis can identify phenogroups of patients with HFpEF with distinct clinical characteristics and long-term outcomes. [ABSTRACT FROM AUTHOR] |
|
Copyright of European Journal of Heart Failure is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) |
| قاعدة البيانات: |
Complementary Index |
Full Text Finder